# Libraries
import cv2
import numpy as np
import matplotlib.pyplot as plt

path = "Pic.jpg"

img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
if img is None:
    raise FileNotFoundError(f"File not found: {path}")

if img.ndim == 2:  # image 2-D and 1 channel(gray picture)
    h, w = img.shape
    channels = 1
else:  # image more than 2-D and 3 or 4 channels(BGR or BGRA)
    h, w, channels = img.shape

if channels == 1:
    color_info = "Grayscale"
elif channels == 3:
    color_info = "BGR (B=blue G=green R=red)"
elif channels == 4:
    color_info = "BGRA (B=blue G=green R=red A=alpha(transparency))"
else:
    color_info = f"{channels} Not normal channels"

print(f"Size (H×W): {h}×{w}")
print(f"Number of channels: {channels} {color_info}")
print(f"Pixel data types: {img.dtype}")

Size (H×W): 600×800
Number of channels: 3 BGR (B=blue G=green R=red)
Pixel data types: uint8

img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)

_, img_binary = cv2.threshold(img_gray, 128, 255, cv2.THRESH_BINARY)


plt.figure(figsize=(15,5))

plt.subplot(1, 3, 1)
plt.imshow(img_rgb)
plt.title("RGB Image")
plt.axis("off")

plt.subplot(1, 3, 2)
plt.imshow(img_gray, cmap="gray")
plt.title("Grayscale Image")
plt.axis("off")

plt.subplot(1, 3, 3)
plt.imshow(img_binary, cmap="gray")
plt.title("Binary Image")
plt.axis("off")

plt.tight_layout()
plt.show()

img_hsv = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV)

plt.figure(figsize=(12,5))

plt.subplot(1, 2, 1)
plt.imshow(img_rgb)
plt.title("RGB Image")
plt.axis("off")

plt.subplot(1, 2, 2)
plt.imshow(img_hsv)
plt.title("HSV Image")
plt.axis("off")

plt.tight_layout()
plt.show()

R, G, B = cv2.split(img_rgb)

R_colored = np.dstack([R, np.zeros_like(R), np.zeros_like(R)])
G_colored = np.dstack([np.zeros_like(G), G, np.zeros_like(G)])
B_colored = np.dstack([np.zeros_like(B), np.zeros_like(B), B])

plt.figure(figsize=(14,8))

plt.subplot(2, 3, 1)
plt.imshow(R, cmap="gray")
plt.title("R gray")
plt.axis("off")

plt.subplot(2, 3, 4)
plt.imshow(R_colored)
plt.title("Red")
plt.axis("off")

plt.subplot(2, 3, 2)
plt.imshow(G, cmap="gray")
plt.title("G gray")
plt.axis("off")

plt.subplot(2, 3, 5)
plt.imshow(G_colored)
plt.title("Green")
plt.axis("off")

plt.subplot(2, 3, 3)
plt.imshow(B, cmap="gray")
plt.title("B gray")
plt.axis("off")

plt.subplot(2, 3, 6)
plt.imshow(B_colored)
plt.title("Blue")
plt.axis("off")

plt.tight_layout()
plt.show()

H, S, V = cv2.split(img_hsv)

plt.figure(figsize=(14,5))

plt.subplot(1, 4, 1)
plt.imshow(cv2.cvtColor(img_hsv, cv2.COLOR_HSV2RGB))
plt.title("HSV Image")
plt.axis("off")

plt.subplot(1, 4, 2)
plt.imshow(H, cmap="gray", vmin=0, vmax=179)
plt.title("H (Hue: 0..179)")
plt.axis("off")

plt.subplot(1, 4, 3)
plt.imshow(S, cmap="gray", vmin=0, vmax=255)
plt.title("S (Saturation: 0..255)")
plt.axis("off")

plt.subplot(1, 4, 4)
plt.imshow(V, cmap="gray", vmin=0, vmax=255)
plt.title("V (Value: 0..255)")
plt.axis("off")

plt.tight_layout()
plt.show()

gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY).astype(np.float32) / 255.0  # normalize to [0,1]

def rms_contrast(I01: np.ndarray):
    return float(I01.std())

C0 = rms_contrast(gray)

def adjust_contrast_linear_rgb(img_rgb_uint8: np.ndarray, alpha:float, pivot:float = 0.5):
    img01 = img_rgb_uint8.astype(np.float32) / 255.0
    out01 = alpha * (img01 - pivot) + pivot
    out01 = np.clip(out01, 0.0, 1.0)
    return (out01 * 255.0).round().astype(np.uint8)

alpha_increase = 1.8
alpha_decrease = 0.4

img_more_contrast = adjust_contrast_linear_rgb(img_rgb, alpha=alpha_increase, pivot=0.5)
img_less_contrast = adjust_contrast_linear_rgb(img_rgb, alpha=alpha_decrease, pivot=0.5)

gray_more = cv2.cvtColor(img_more_contrast, cv2.COLOR_RGB2GRAY).astype(np.float32) / 255.0
gray_less = cv2.cvtColor(img_less_contrast, cv2.COLOR_RGB2GRAY).astype(np.float32) / 255.0

C_more = rms_contrast(gray_more)
C_less = rms_contrast(gray_less)

print(f"Orginal Contrast(RMS): {C0:.4f}")
print(f"Increased Contrast(α={alpha_increase}): {C_more:.4f}")
print(f"Decreased Contrast(α={alpha_decrease}): {C_less:.4f}")

plt.figure(figsize=(16,8))

plt.subplot(2,3,1)
plt.imshow(img_rgb)
plt.title(f"Orginal(RMS={C0:.4f})")
plt.axis("off")

plt.subplot(2,3,2)
plt.imshow(img_more_contrast)
plt.title(f"Increased Contrast (α={alpha_increase}, RMS={C_more:.4f})")
plt.axis("off")

plt.subplot(2,3,3)
plt.imshow(img_less_contrast)
plt.title(f"Decreased Contrast (α={alpha_decrease}, RMS={C_less:.4f})")
plt.axis("off")

plt.subplot(2,3,4)
plt.imshow(gray, cmap="gray", vmin=0, vmax=1)
plt.title("Orginal Gray")
plt.axis("off")

plt.subplot(2,3,5)
plt.imshow(gray, cmap="gray", vmin=0, vmax=1)
plt.title("Increased Gray")
plt.axis("off")

plt.subplot(2,3,6)
plt.imshow(gray_less, cmap="gray", vmin=0, vmax=1)
plt.title("Decreased Gray")
plt.axis("off")

plt.tight_layout()
plt.show()

Orginal Contrast(RMS): 0.2296
Increased Contrast(α=1.8): 0.3088
Decreased Contrast(α=0.4): 0.0919

gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)

def hist_uint8(arr):
    counts, bin_edges = np.histogram(arr.ravel(), bins=256, range=(0, 255))
    return counts, bin_edges

hist_gray, bins_gray = hist_uint8(gray)
R, G, B = cv2.split(img_rgb)
hist_R, _ = hist_uint8(R)
hist_G, _ = hist_uint8(G)
hist_B, _ = hist_uint8(B)

plt.figure(figsize=(14,5))

plt.subplot(1,2,1)
plt.imshow(img_rgb)
plt.title("Input Image(RGB)")
plt.axis("off")

plt.subplot(1,2,2)
plt.plot(bins_gray[:-1], hist_gray)
plt.title("Gray Image Histogram")
plt.xlabel("Brightness(0..255)")
plt.ylabel("Number of Pixels")
plt.grid(True, linewidth=0.3)

plt.tight_layout()
plt.show()

plt.figure(figsize=(7,5))
plt.plot(hist_B, label="B")
plt.plot(hist_R, label="R")
plt.plot(hist_G, label="G")
plt.title("RGB Histograms")
plt.xlabel("Brightness(0..255)")
plt.ylabel("Number of Pixels")
plt.legend()
plt.grid(True, linewidth=0.3)
plt.tight_layout()
plt.show()

gray_eq = cv2.equalizeHist(gray)

img_ycrcb = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2YCrCb)
Y, Cr, Cb = cv2.split(img_ycrcb)
Y_eq = cv2.equalizeHist(Y)
img_ycrcb_eq = cv2.merge([Y_eq, Cr, Cb])
rgb_eqY = cv2.cvtColor(img_ycrcb_eq, cv2.COLOR_YCrCb2RGB)


def stretch_minmax(img_u8):
    i_min = img_u8.min()
    i_max = img_u8.max()
    if i_max == i_min:
        return img_u8.copy()
    out = (img_u8.astype(np.float32) - i_min) / (i_max - i_min) * 255.0
    return np.clip(out, 0, 255).astype(np.uint8)

def stretch_percentile(img_u8, p_low=2, p_high=98):
    lo = np.percentile(img_u8, p_low)
    hi = np.percentile(img_u8, p_high)
    if hi <= lo:
        return img_u8.copy()
    out = (img_u8.astype(np.float32) - lo) / (hi - lo) * 255.0
    return np.clip(out, 0, 255).astype(np.uint8)

gray_stretch = stretch_minmax(gray)
gray_stretch_robust = stretch_percentile(gray, 2, 98)

Y_stretch = stretch_percentile(Y, 2, 98)
img_ycrcb_st = cv2.merge([Y_stretch, Cr, Cb])
rgb_stretch = cv2.cvtColor(img_ycrcb_st, cv2.COLOR_YCrCb2RGB)

hist_gray, bins_gray = hist_uint8(gray)
hist_gray_eq, _ = hist_uint8(gray_eq)
hist_gray_st, _ = hist_uint8(gray_stretch)
hist_gray_st_r, _ = hist_uint8(gray_stretch_robust)

plt.figure(figsize=(16,10))

plt.subplot(2,4,1); plt.imshow(img_rgb)
plt.title("Original RGB")
plt.axis("off")

plt.subplot(2,4,2); plt.imshow(gray, cmap="gray")
plt.title("Original Grayscale")
plt.axis("off")

plt.subplot(2,4,3); plt.imshow(gray_eq, cmap="gray")
plt.title("Equalized (Gray)")
plt.axis("off")

plt.subplot(2,4,4); plt.imshow(rgb_eqY)
plt.title("Equalized on Y channel (Color)")
plt.axis("off")

plt.subplot(2,4,5); plt.imshow(gray_stretch, cmap="gray")
plt.title("Stretch (Gray, Min-Max)")
plt.axis("off")

plt.subplot(2,4,6); plt.imshow(gray_stretch_robust, cmap="gray")
plt.title("Stretch (Gray, 2%-98%)")
plt.axis("off")

plt.subplot(2,4,7); plt.imshow(rgb_stretch)
plt.title("Stretch on Y channel (Color)")
plt.axis("off")

plt.tight_layout()
plt.show()

plt.figure(figsize=(10,6))

plt.plot(bins_gray[:-1], hist_gray_eq, label="Equalized", linewidth=2)
plt.plot(bins_gray[:-1], hist_gray_st, label="Stretch Min-Max", linewidth=2)
plt.plot(bins_gray[:-1], hist_gray_st_r, label="Stretch 2%-98%", linewidth=2)

plt.title("Grayscale Histogram Comparison: Before vs After", fontsize=13)
plt.xlabel("Intensity (0–255)", fontsize=12)
plt.ylabel("Pixel Count", fontsize=12)
plt.legend()
plt.grid(True, linewidth=0.4)
plt.tight_layout()
plt.show()

بخش اول